1. Technical Field
The present invention relates to a method and a device for controlling an equipment, and in particular, to a method and a device for controlling an equipment based on multiple-input/one-output control.
2. Background Art
Recent automatic air conditioners for automobiles are arranged to control their blowing air quantity and blowing air temperature to the adequate values corresponding to automobiles' environments, referring to various sensor inputs such as inputs from an ambient temperature sensor, a solar radiation quantity sensor and a room temperature sensor. This control system requires a control specification to connect the set values of the blowing air quantity value and blowing air temperature value (which are defined by the opening degree of an air mix damper) to the sensor input values. Conventionally widely used air conditioner control systems include a TAO method (described below). An automotive air conditioner controls the blowing air temperature and air quantity based on the temperature information of a room temperature sensor so that the room temperature comes closer to the aimed set temperature.
In this air conditioner, target blowing air temperature (TAO) is calculated by the following formula.TAO=E×(TSET+ΔT)−F×TR−G×TAM−H×TS+C 
(TSET: set temperature; TR: room temperature; TAM: ambient temperature; TS: solar radiation quantity; ΔT and C: correction constants; E to H: coefficients)
A neural network is also known as another system which is appropriate for the multiple-input/one-output control.
The TAO method has to define control coefficient of ΔT, C, E to H and others as different values depending on types of automobiles.
However, the TAO is a multivariable function having four input variables, as the degrees of freedom, such as the set temperature (TSET), room temperature (TR), ambient temperature (TAM), and solar temperature (TS). Finding suitable values for multi variables while making the variables change independently requires considerable manpower, even though simulations can be used, and considerable time has to be spent in developing the control logic using the TAO.
The controller that uses the neural network tends to increase, at an exponential rate, the number of processing elements to be needed, as the number of inputs increases. Further, this system requires complicated learning processes to be repeated many times until desired input results for various input combinations are obtained, which requires a long lead time for the development. Further, an application of the learning processes requires a computer with high performance, which causes installation cost to be high.